000			03687nam a22002657a 4500
005			20250409164737.0
008			250409b |||||||| |||| 00| 0 eng d
020			_a9781032542263
082			_a006.3 _bBAN
100			_aBanerjee, Tania _922963
245			_aVideo based machine learning for traffic intersections
260			_bRoutledge _aNew York _c2024
300			_axxvi, 167 p.
365			_aGBP _b110.00
500			_aTable of contents: 1. Introduction 2. Detection, Tracking, and Classification 3. Near-miss Detection 4. Severe Events 5. Performance-Safety Trade-offs 6. Trajectory Prediction 7. Vehicle Tracking across Multiple Intersections 8. User Interface 9. Conclusion (https://www.routledge.com/Video-Based-Machine-Learning-for-Traffic-Intersections/Banerjee-Huang-Wu-Chen-Rangarajan-Ranka/p/book/9781032542263?srsltid=AfmBOooYRA-l5jj_kpYwFD6j4aPGCwypQ9cRnpyumWsSdSQVDtMBxhip)
520			_aVideo Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts (https://www.routledge.com/Video-Based-Machine-Learning-for-Traffic-Intersections/Banerjee-Huang-Wu-Chen-Rangarajan-Ranka/p/book/9781032542263?srsltid=AfmBOooYRA-l5jj_kpYwFD6j4aPGCwypQ9cRnpyumWsSdSQVDtMBxhip)
650			_aTraffic signals _922964
650			_aComputer vision _95987
700			_aHuang, Xiaohui _922965
700			_aWu, Aotian _922966
700			_aKe, Chen _922967
700			_aRangarajan, Anand _922968
700			_aRanka, Sanjay _922969
942			_cBK _2ddc
999			_c9075 _d9075